Full-Time
Senior Data Architect
Azure / CPG Industry
Fractal Analytics
5,001-10,000 employees
Enterprise AI solutions for decision making
No salary listed
London, UK
In Person
 Data & Analytics (1) |
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- Proficiency with PySpark, Databricks, and Azure Data Platform (Azure Data Factory, Azure Data Lake Storage, and Synapse)
- Hands-on development, debugging, optimization, and production support of data pipelines
- Design and build end-to-end data pipelines (batch and near real-time) using PySpark, Databricks, and Azure Data Platform (ADF, ADLS, Synapse)
- Be hands-on in development, debugging, optimization, and production support of data pipelines
- Work with or extend existing/proprietary ETL frameworks (e.g., Mar's Simpel or similar) and improve performance and reliability
- Implement data modeling, transformation, and orchestration patterns aligned with best practices
- Apply data engineering fundamentals including partitioning, indexing, caching, cost optimization, and performance tuning
- Collaborate with upstream and downstream teams to ensure data quality, reliability, and SLAs
- Contribute to the design of cloud-native data architectures covering ingestion, processing, storage, and consumption
- Translate business and analytical requirements into practical, scalable data solutions
- Support data governance practices including metadata, lineage, data quality checks, and access controls
- Work within hybrid environments (on-prem to cloud) and support modernization initiatives
- Understand and apply data mesh concepts where relevant (domain ownership, reusable data products, basic contracts)
- Evaluate tools and frameworks with a build vs. buy mindset, recommending pragmatic solutions
- Act as a technical anchor for a data engineering team
- Provide technical guidance, code reviews, and mentoring to engineers
- Own delivery for assigned data products or pipelines — from design through deployment
- Collaborate with product owners, analysts, and architects to clarify requirements and priorities
- Engage with business and analytics stakeholders to understand data needs and translate them into technical solutions
- Clearly communicate technical designs and trade-offs to both technical and non-technical audiences
- Escalate risks and propose mitigation strategies proactively
- Support documentation of architecture, pipelines, and operational processes
- Exposure to AI/ML data workflows (feature engineering, model inputs, MLOps basics)
- Awareness of LLMs / Agentic AI architectures from a data platform perspective
- Experience with other platforms such as AWS, GCP, Snowflake, BigQuery, Redshift
- Familiarity with data governance or catalog tools (DataHub, Collibra, dbt, etc.)
- Experience working in CPG, Retail, Supply Chain, or similar domains
Fractal Analytics helps large enterprises use AI to improve decision making by combining AI, data engineering, and design across multiple products. Its offerings include Crux Intelligence for AI-driven insights, Eugenie.ai for sustainability, Asper.ai for revenue growth, and Senseforth.ai for conversational AI, plus Qure.ai in healthcare. The company applies machine learning, NLP, and data science to extract insights, automate tasks, and power enterprise conversations across analytics, sustainability, revenue, and customer interactions. Its goal is to power every human decision in the enterprise by delivering a broad enterprise-focused AI portfolio with a global delivery footprint and recognition from industry analysts and awards.
Company Size
5,001-10,000
Company Stage
IPO
Headquarters
New York City, New York
Founded
2000
Simplify's Take
What believers are saying
- Q3 revenue grew 21% YoY; Healthcare segment surged 78% YoY.
- Six clients now generate $20M+ revenue each, up from four.
- Triple-digit SaaS subscription growth offers valuation re-rating potential.
What critics are saying
- 10% revenue from single Magnificent 7 client risks sudden loss.
- PE shareholders flood market with ₹1,810 Cr OFS post-May 13.
- OpenAI's o1 models commoditize Fractal's consulting via cheaper APIs.
What makes Fractal Analytics unique
- India's first listed enterprise AI company with Fortune 500 client base.
- PiEvolve autonomous ML engine surpasses 60% on OpenAI's MLE-Bench.
- Healthcare AI model outperforms ChatGPT-5 and Gemini Pro 3.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
401(k) Retirement Plan
401(k) Company Match
Paid Holidays
Parental Leave
Unlimited Paid Time Off
Growth & Insights and Company News
6 month growth
↑ 1%1 year growth
↑ 1%2 year growth
↑ 1%Fractal Analytics, a newly listed AI company, reported consolidated revenue of INR 8,544 million for Q3 FY2026, growing 21% year-on-year. The company's profit after tax reached INR 1,001 million, whilst adjusted EBITDA grew 24% to INR 1,521 million. Growth was driven by the Healthcare and Life Sciences segment, which grew 78% year-on-year, and Banking and Financial Services, up 26%. Fractal expanded its gross margin to 47.2% and achieved a net revenue retention of 114%. The company's AI health assistant became the first model to score above 50 on OpenAI's HealthBench (Hard), outperforming ChatGPT-5 and Gemini Pro 3. Fractal now serves six clients generating over $20 million in revenue each, up from four previously.
Fractal, a global enterprise AI company, has launched PiEvolve, an evolutionary agentic engine for autonomous machine learning and scientific discovery. The system ranks among top-performing agents on OpenAI's MLE-Bench, becoming the first evaluated agent to surpass 60% in Overall Medal Rate and 80% in MLE-Bench-Lite performance. PiEvolve continuously tests and improves solutions using a graph-structured search architecture that integrates reasoning, code generation and validation. It delivers competitive results within 24 hours whilst maintaining efficiency, with strong performance evident after just 12 hours of runtime. The system features continuous optimisation, intelligent memory to avoid local optima, dual strategy for debugging, and production-ready capabilities including pause and resume functions. Fractal aims to deploy PiEvolve across complex optimisation problems in supply chains, financial services and data centre operations.
Fractal Analytics' initial public offering received a muted response on its first day of bidding on Monday, garnering just 9% subscription. The AI solutions provider received bids for 15.8 lakh shares against 1.86 crore shares on offer. The retail individual investors category attracted 35% subscription, whilst the non-institutional investors quota achieved 7% subscription. The three-day IPO continues through the week.
Fractal Analytics has opened a ₹2,834-crore IPO at ₹857-₹900 per share, down from an original ₹4,900-crore target. The Mumbai-based analytics firm will allocate ₹355 crore—approximately 35% of proceeds—to AI development, research and investments, with remaining funds servicing debt and capital expenditure. The company is focusing resources on its Cogentic platform, which provides AI solutions for invoice-to-cash, customer experience and revenue growth. Fractal aims to expand margins and strengthen its position in healthcare, banking and consumer goods sectors. The IPO's timing benefits from easing US-India trade tensions and improved client spending conditions. The successful pricing may encourage further AI-focused listings in India's public markets.
Enterprise AI solutions unicorn Fractal Analytics has reduced its initial public offering size by 42% to ₹2,834 crore, according to a prospectus filed with SEBI on 2 February. The IPO comprises a fresh issue of ₹1,024 crore and an offer for sale of ₹1,810 crore. Major shareholders Quinag Bidco (managed by Apax Partners), TPG Fett Holdings and GLM Family Trust will sell stakes through the offer. The IPO opens on 9 February and closes on 11 February, with anchor bidding beginning on 6 February. The company had initially filed for an IPO size of ₹4,900 crore. Fractal has been selected by the Indian government to build India's first large reasoning model under the India AI Mission.